Overview

Dataset statistics

Number of variables16
Number of observations105666
Missing cells303
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 MiB
Average record size in memory128.0 B

Variable types

Categorical4
Numeric12

Alerts

Province has a high cardinality: 90 distinct values High cardinality
City has a high cardinality: 1441 distinct values High cardinality
Barangay has a high cardinality: 26279 distinct values High cardinality
DOMAGOSO, ISKO MORENO (AKSYON) is highly correlated with LACSON, PING (PDR)High correlation
LACSON, PING (PDR) is highly correlated with DOMAGOSO, ISKO MORENO (AKSYON)High correlation
MARCOS, BONGBONG (PFP) is highly correlated with ratio_robredo_marcosHigh correlation
ROBREDO, LENI (IND) is highly correlated with ratio_robredo_marcosHigh correlation
ratio_robredo_marcos is highly correlated with MARCOS, BONGBONG (PFP) and 1 other fieldsHigh correlation
MARCOS, BONGBONG (PFP) is highly correlated with ratio_robredo_marcosHigh correlation
ROBREDO, LENI (IND) is highly correlated with ratio_robredo_marcosHigh correlation
ratio_robredo_marcos is highly correlated with MARCOS, BONGBONG (PFP) and 1 other fieldsHigh correlation
Province is highly correlated with RegionHigh correlation
Region is highly correlated with ProvinceHigh correlation
Region is highly correlated with Province and 3 other fieldsHigh correlation
Province is highly correlated with Region and 6 other fieldsHigh correlation
Precinct ID is highly correlated with Region and 1 other fieldsHigh correlation
DOMAGOSO, ISKO MORENO (AKSYON) is highly correlated with ProvinceHigh correlation
MANGONDATO, FAISAL (KTPNAN) is highly correlated with ProvinceHigh correlation
MARCOS, BONGBONG (PFP) is highly correlated with Region and 2 other fieldsHigh correlation
PACQUIAO, MANNY PACMAN(PROMDI) is highly correlated with ProvinceHigh correlation
ROBREDO, LENI (IND) is highly correlated with Region and 2 other fieldsHigh correlation
GONZALES, NORBERTO (PDSP) is highly skewed (γ1 = 40.24535664) Skewed
Precinct ID has unique values Unique
ABELLA, ERNIE (IND) has 47643 (45.1%) zeros Zeros
DE GUZMAN, LEODY (PLM) has 50828 (48.1%) zeros Zeros
DOMAGOSO, ISKO MORENO (AKSYON) has 4429 (4.2%) zeros Zeros
GONZALES, NORBERTO (PDSP) has 52143 (49.3%) zeros Zeros
LACSON, PING (PDR) has 9080 (8.6%) zeros Zeros
MANGONDATO, FAISAL (KTPNAN) has 73064 (69.1%) zeros Zeros
MONTEMAYOR, JOSE JR. (DPP) has 66565 (63.0%) zeros Zeros
PACQUIAO, MANNY PACMAN(PROMDI) has 4461 (4.2%) zeros Zeros

Reproduction

Analysis started2022-05-24 09:43:40.817758
Analysis finished2022-05-24 09:44:09.654553
Duration28.84 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size825.6 KiB
REGION IV-A
13906 
REGION III
10853 
NATIONAL CAPITAL REGION
10388 
REGION VI
8558 
REGION VII
8019 
Other values (13)
53942 

Length

Max length32
Median length11
Mean length11.19338292
Min length3

Characters and Unicode

Total characters1182760
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowREGION X
2nd rowREGION X
3rd rowREGION X
4th rowREGION X
5th rowREGION X

Common Values

ValueCountFrequency (%)
REGION IV-A13906
13.2%
REGION III10853
10.3%
NATIONAL CAPITAL REGION10388
9.8%
REGION VI8558
 
8.1%
REGION VII8019
 
7.6%
REGION V6653
 
6.3%
REGION VIII6255
 
5.9%
REGION I6117
 
5.8%
REGION X4933
 
4.7%
REGION XI4762
 
4.5%
Other values (8)25222
23.9%

Length

2022-05-24T17:44:09.744809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
region100786
46.0%
iv-a13906
 
6.4%
iii10853
 
5.0%
national10388
 
4.7%
capital10388
 
4.7%
vi8558
 
3.9%
vii8019
 
3.7%
v6653
 
3.0%
viii6255
 
2.9%
i6117
 
2.8%
Other values (11)37063
 
16.9%

Most occurring characters

ValueCountFrequency (%)
I263141
22.2%
N123708
10.5%
O114181
9.7%
113320
9.6%
R111243
9.4%
E105078
 
8.9%
G100786
 
8.5%
A66776
 
5.6%
V49735
 
4.2%
T25068
 
2.1%
Other values (9)109724
9.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1052197
89.0%
Space Separator113320
 
9.6%
Dash Punctuation17243
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I263141
25.0%
N123708
11.8%
O114181
10.9%
R111243
10.6%
E105078
 
10.0%
G100786
 
9.6%
A66776
 
6.3%
V49735
 
4.7%
T25068
 
2.4%
L25068
 
2.4%
Other values (7)67413
 
6.4%
Space Separator
ValueCountFrequency (%)
113320
100.0%
Dash Punctuation
ValueCountFrequency (%)
-17243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1052197
89.0%
Common130563
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I263141
25.0%
N123708
11.8%
O114181
10.9%
R111243
10.6%
E105078
 
10.0%
G100786
 
9.6%
A66776
 
6.3%
V49735
 
4.7%
T25068
 
2.4%
L25068
 
2.4%
Other values (7)67413
 
6.4%
Common
ValueCountFrequency (%)
113320
86.8%
-17243
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1182760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I263141
22.2%
N123708
10.5%
O114181
9.7%
113320
9.6%
R111243
9.4%
E105078
 
8.9%
G100786
 
8.5%
A66776
 
5.6%
V49735
 
4.2%
T25068
 
2.1%
Other values (9)109724
9.3%

Province
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size825.6 KiB
CEBU
 
4692
CAVITE
 
3371
NCR - SECOND DISTRICT
 
3354
PANGASINAN
 
3330
ILOILO
 
3058
Other values (85)
87861 

Length

Max length23
Median length18
Mean length10.4854447
Min length4

Characters and Unicode

Total characters1107955
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBUKIDNON
2nd rowBUKIDNON
3rd rowBUKIDNON
4th rowBUKIDNON
5th rowBUKIDNON

Common Values

ValueCountFrequency (%)
CEBU4692
 
4.4%
CAVITE3371
 
3.2%
NCR - SECOND DISTRICT3354
 
3.2%
PANGASINAN3330
 
3.2%
ILOILO3058
 
2.9%
LAGUNA2958
 
2.8%
NEGROS OCCIDENTAL2901
 
2.7%
BATANGAS2855
 
2.7%
BULACAN2835
 
2.7%
LEYTE2535
 
2.4%
Other values (80)73777
69.8%

Length

2022-05-24T17:44:10.061854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
del11004
 
6.0%
10388
 
5.7%
ncr9705
 
5.3%
sur9611
 
5.3%
district7922
 
4.3%
norte6032
 
3.3%
davao4762
 
2.6%
cebu4692
 
2.6%
oriental4583
 
2.5%
occidental4510
 
2.5%
Other values (87)109402
59.9%

Most occurring characters

ValueCountFrequency (%)
A158824
14.3%
N95597
 
8.6%
76945
 
6.9%
O75327
 
6.8%
I74901
 
6.8%
E71281
 
6.4%
R69040
 
6.2%
T63829
 
5.8%
S61162
 
5.5%
L56735
 
5.1%
Other values (17)304314
27.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1020222
92.1%
Space Separator76945
 
6.9%
Dash Punctuation10788
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A158824
15.6%
N95597
9.4%
O75327
 
7.4%
I74901
 
7.3%
E71281
 
7.0%
R69040
 
6.8%
T63829
 
6.3%
S61162
 
6.0%
L56735
 
5.6%
C56670
 
5.6%
Other values (15)236856
23.2%
Space Separator
ValueCountFrequency (%)
76945
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1020222
92.1%
Common87733
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A158824
15.6%
N95597
9.4%
O75327
 
7.4%
I74901
 
7.3%
E71281
 
7.0%
R69040
 
6.8%
T63829
 
6.3%
S61162
 
6.0%
L56735
 
5.6%
C56670
 
5.6%
Other values (15)236856
23.2%
Common
ValueCountFrequency (%)
76945
87.7%
-10788
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1107955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A158824
14.3%
N95597
 
8.6%
76945
 
6.9%
O75327
 
6.8%
I74901
 
6.8%
E71281
 
6.4%
R69040
 
6.2%
T63829
 
5.8%
S61162
 
5.5%
L56735
 
5.1%
Other values (17)304314
27.5%

City
Categorical

HIGH CARDINALITY

Distinct1441
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size825.6 KiB
QUEZON CITY
 
1904
CITY OF DAVAO
 
1345
CITY OF CALOOCAN
 
1018
CITY OF CEBU
 
909
CITY OF ANTIPOLO
 
650
Other values (1436)
99840 

Length

Max length31
Median length23
Mean length10.33283175
Min length3

Characters and Unicode

Total characters1091829
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBAUNGON
2nd rowBAUNGON
3rd rowBAUNGON
4th rowBAUNGON
5th rowBAUNGON

Common Values

ValueCountFrequency (%)
QUEZON CITY1904
 
1.8%
CITY OF DAVAO1345
 
1.3%
CITY OF CALOOCAN1018
 
1.0%
CITY OF CEBU909
 
0.9%
CITY OF ANTIPOLO650
 
0.6%
TONDO628
 
0.6%
CITY OF ZAMBOANGA625
 
0.6%
CITY OF TAGUIG622
 
0.6%
SANTA CRUZ622
 
0.6%
CITY OF MAKATI615
 
0.6%
Other values (1431)96728
91.5%

Length

2022-05-24T17:44:10.178818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city34593
 
17.9%
of31745
 
16.5%
san6391
 
3.3%
santa2488
 
1.3%
quezon2189
 
1.1%
davao1345
 
0.7%
jose1141
 
0.6%
general1037
 
0.5%
caloocan1018
 
0.5%
cebu909
 
0.5%
Other values (1477)110059
57.1%

Most occurring characters

ValueCountFrequency (%)
A176629
16.2%
O95041
 
8.7%
87249
 
8.0%
I85577
 
7.8%
N80575
 
7.4%
T68087
 
6.2%
C61058
 
5.6%
L48878
 
4.5%
Y46773
 
4.3%
S40753
 
3.7%
Other values (21)301209
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1002432
91.8%
Space Separator87249
 
8.0%
Other Punctuation1086
 
0.1%
Dash Punctuation1062
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A176629
17.6%
O95041
 
9.5%
I85577
 
8.5%
N80575
 
8.0%
T68087
 
6.8%
C61058
 
6.1%
L48878
 
4.9%
Y46773
 
4.7%
S40753
 
4.1%
U34595
 
3.5%
Other values (17)264466
26.4%
Other Punctuation
ValueCountFrequency (%)
.815
75.0%
'271
 
25.0%
Space Separator
ValueCountFrequency (%)
87249
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1062
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1002432
91.8%
Common89397
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A176629
17.6%
O95041
 
9.5%
I85577
 
8.5%
N80575
 
8.0%
T68087
 
6.8%
C61058
 
6.1%
L48878
 
4.9%
Y46773
 
4.7%
S40753
 
4.1%
U34595
 
3.5%
Other values (17)264466
26.4%
Common
ValueCountFrequency (%)
87249
97.6%
-1062
 
1.2%
.815
 
0.9%
'271
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1089451
99.8%
None2378
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A176629
16.2%
O95041
 
8.7%
87249
 
8.0%
I85577
 
7.9%
N80575
 
7.4%
T68087
 
6.2%
C61058
 
5.6%
L48878
 
4.5%
Y46773
 
4.3%
S40753
 
3.7%
Other values (20)298831
27.4%
None
ValueCountFrequency (%)
Ñ2378
100.0%

Barangay
Categorical

HIGH CARDINALITY

Distinct26279
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size825.6 KiB
POBLACION
 
2914
SAN ISIDRO
 
977
SAN JOSE
 
681
SAN ROQUE
 
526
SAN VICENTE
 
504
Other values (26274)
100064 

Length

Max length45
Median length39
Mean length9.533492325
Min length2

Characters and Unicode

Total characters1007366
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9611 ?
Unique (%)9.1%

Sample

1st rowBALINTAD
2nd rowBUENAVISTA
3rd rowDANATAG
4th rowDANATAG
5th rowDANATAG

Common Values

ValueCountFrequency (%)
POBLACION2914
 
2.8%
SAN ISIDRO977
 
0.9%
SAN JOSE681
 
0.6%
SAN ROQUE526
 
0.5%
SAN VICENTE504
 
0.5%
SAN ANTONIO500
 
0.5%
SAN JUAN467
 
0.4%
SANTO NIÑO438
 
0.4%
SANTA CRUZ387
 
0.4%
SAN MIGUEL336
 
0.3%
Other values (26269)97936
92.7%

Length

2022-05-24T17:44:10.303492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san8568
 
5.5%
pob7750
 
4.9%
barangay5810
 
3.7%
poblacion4755
 
3.0%
santa2016
 
1.3%
i1238
 
0.8%
ii1228
 
0.8%
santo1196
 
0.8%
isidro1171
 
0.7%
jose1041
 
0.7%
Other values (21493)122244
77.9%

Most occurring characters

ValueCountFrequency (%)
A187602
18.6%
N100132
 
9.9%
O74255
 
7.4%
I62394
 
6.2%
51598
 
5.1%
B50238
 
5.0%
L48571
 
4.8%
G45908
 
4.6%
S43194
 
4.3%
T36930
 
3.7%
Other values (36)306544
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter910233
90.4%
Space Separator54542
 
5.4%
Decimal Number13496
 
1.3%
Other Punctuation9777
 
1.0%
Open Punctuation7308
 
0.7%
Close Punctuation7308
 
0.7%
Dash Punctuation4702
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A187602
20.6%
N100132
11.0%
O74255
 
8.2%
I62394
 
6.9%
B50238
 
5.5%
L48571
 
5.3%
G45908
 
5.0%
S43194
 
4.7%
T36930
 
4.1%
R34263
 
3.8%
Other values (17)226746
24.9%
Decimal Number
ValueCountFrequency (%)
12888
21.4%
21621
12.0%
71359
10.1%
31345
10.0%
61270
9.4%
81173
8.7%
41145
 
8.5%
51132
 
8.4%
9815
 
6.0%
0748
 
5.5%
Other Punctuation
ValueCountFrequency (%)
.9711
99.3%
'56
 
0.6%
/8
 
0.1%
*2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
51598
94.6%
 2944
 
5.4%
Open Punctuation
ValueCountFrequency (%)
(7308
100.0%
Close Punctuation
ValueCountFrequency (%)
)7308
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4702
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin910233
90.4%
Common97133
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A187602
20.6%
N100132
11.0%
O74255
 
8.2%
I62394
 
6.9%
B50238
 
5.5%
L48571
 
5.3%
G45908
 
5.0%
S43194
 
4.7%
T36930
 
4.1%
R34263
 
3.8%
Other values (17)226746
24.9%
Common
ValueCountFrequency (%)
51598
53.1%
.9711
 
10.0%
(7308
 
7.5%
)7308
 
7.5%
-4702
 
4.8%
 2944
 
3.0%
12888
 
3.0%
21621
 
1.7%
71359
 
1.4%
31345
 
1.4%
Other values (9)6349
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1003311
99.6%
None4055
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A187602
18.7%
N100132
 
10.0%
O74255
 
7.4%
I62394
 
6.2%
51598
 
5.1%
B50238
 
5.0%
L48571
 
4.8%
G45908
 
4.6%
S43194
 
4.3%
T36930
 
3.7%
Other values (34)302489
30.1%
None
ValueCountFrequency (%)
 2944
72.6%
Ñ1111
 
27.4%

Precinct ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct105666
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41654134.65
Minimum1010001
Maximum93150021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:10.422493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1010001
5-th percentile7120015.25
Q122170748.25
median39141465.5
Q358130083.75
95-th percentile76050121.75
Maximum93150021
Range92140020
Interquartile range (IQR)35959335.5

Descriptive statistics

Standard deviation22775840.69
Coefficient of variation (CV)0.546784632
Kurtosis-1.078557382
Mean41654134.65
Median Absolute Deviation (MAD)17941405
Skewness0.1373227102
Sum4.401425792 × 1012
Variance5.187389189 × 1014
MonotonicityNot monotonic
2022-05-24T17:44:10.543101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130100091
 
< 0.1%
920100251
 
< 0.1%
920101031
 
< 0.1%
920101021
 
< 0.1%
920101011
 
< 0.1%
920100871
 
< 0.1%
920100701
 
< 0.1%
920100691
 
< 0.1%
920100681
 
< 0.1%
920100501
 
< 0.1%
Other values (105656)105656
> 99.9%
ValueCountFrequency (%)
10100011
< 0.1%
10100021
< 0.1%
10100031
< 0.1%
10100041
< 0.1%
10100051
< 0.1%
10100061
< 0.1%
10100071
< 0.1%
10100081
< 0.1%
10100091
< 0.1%
10100101
< 0.1%
ValueCountFrequency (%)
931500211
< 0.1%
931500201
< 0.1%
931500191
< 0.1%
931500181
< 0.1%
931500171
< 0.1%
931500161
< 0.1%
931500151
< 0.1%
931500141
< 0.1%
931500101
< 0.1%
931500081
< 0.1%

ABELLA, ERNIE (IND)
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.069568262
Minimum0
Maximum33
Zeros47643
Zeros (%)45.1%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:10.652616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.452748253
Coefficient of variation (CV)1.358256695
Kurtosis17.9236327
Mean1.069568262
Median Absolute Deviation (MAD)1
Skewness2.780923701
Sum113017
Variance2.110477487
MonotonicityNot monotonic
2022-05-24T17:44:10.748653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
047643
45.1%
130056
28.4%
214742
 
14.0%
36801
 
6.4%
43214
 
3.0%
51537
 
1.5%
6760
 
0.7%
7366
 
0.3%
8212
 
0.2%
9133
 
0.1%
Other values (18)202
 
0.2%
ValueCountFrequency (%)
047643
45.1%
130056
28.4%
214742
 
14.0%
36801
 
6.4%
43214
 
3.0%
51537
 
1.5%
6760
 
0.7%
7366
 
0.3%
8212
 
0.2%
9133
 
0.1%
ValueCountFrequency (%)
331
 
< 0.1%
291
 
< 0.1%
261
 
< 0.1%
252
< 0.1%
233
< 0.1%
221
 
< 0.1%
211
 
< 0.1%
201
 
< 0.1%
192
< 0.1%
184
< 0.1%

DE GUZMAN, LEODY (PLM)
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.869702648
Minimum0
Maximum66
Zeros50828
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:10.854661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum66
Range66
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.204040839
Coefficient of variation (CV)1.384428163
Kurtosis203.391695
Mean0.869702648
Median Absolute Deviation (MAD)1
Skewness6.42667231
Sum91898
Variance1.449714343
MonotonicityNot monotonic
2022-05-24T17:44:10.951661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
050828
48.1%
132125
30.4%
214210
 
13.4%
35377
 
5.1%
41941
 
1.8%
5667
 
0.6%
6257
 
0.2%
7124
 
0.1%
848
 
< 0.1%
921
 
< 0.1%
Other values (21)68
 
0.1%
ValueCountFrequency (%)
050828
48.1%
132125
30.4%
214210
 
13.4%
35377
 
5.1%
41941
 
1.8%
5667
 
0.6%
6257
 
0.2%
7124
 
0.1%
848
 
< 0.1%
921
 
< 0.1%
ValueCountFrequency (%)
661
< 0.1%
581
< 0.1%
511
< 0.1%
411
< 0.1%
342
< 0.1%
321
< 0.1%
301
< 0.1%
272
< 0.1%
261
< 0.1%
241
< 0.1%

DOMAGOSO, ISKO MORENO (AKSYON)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct319
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.84268355
Minimum0
Maximum351
Zeros4429
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:11.065661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q323
95-th percentile43
Maximum351
Range351
Interquartile range (IQR)18

Descriptive statistics

Standard deviation24.39606704
Coefficient of variation (CV)1.367286876
Kurtosis45.97286638
Mean17.84268355
Median Absolute Deviation (MAD)8
Skewness5.73448762
Sum1885365
Variance595.1680871
MonotonicityNot monotonic
2022-05-24T17:44:11.181704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34738
 
4.5%
24687
 
4.4%
44600
 
4.4%
54589
 
4.3%
04429
 
4.2%
14397
 
4.2%
64192
 
4.0%
74074
 
3.9%
83825
 
3.6%
93683
 
3.5%
Other values (309)62452
59.1%
ValueCountFrequency (%)
04429
4.2%
14397
4.2%
24687
4.4%
34738
4.5%
44600
4.4%
54589
4.3%
64192
4.0%
74074
3.9%
83825
3.6%
93683
3.5%
ValueCountFrequency (%)
3511
< 0.1%
3471
< 0.1%
3451
< 0.1%
3401
< 0.1%
3391
< 0.1%
3342
< 0.1%
3331
< 0.1%
3292
< 0.1%
3282
< 0.1%
3251
< 0.1%

GONZALES, NORBERTO (PDSP)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8408475763
Minimum0
Maximum197
Zeros52143
Zeros (%)49.3%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:11.293319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum197
Range197
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.349179798
Coefficient of variation (CV)1.604547407
Kurtosis5161.257074
Mean0.8408475763
Median Absolute Deviation (MAD)1
Skewness40.24535664
Sum88849
Variance1.820286128
MonotonicityNot monotonic
2022-05-24T17:44:11.396328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
052143
49.3%
131995
30.3%
213471
 
12.7%
35064
 
4.8%
41775
 
1.7%
5699
 
0.7%
6264
 
0.2%
7121
 
0.1%
856
 
0.1%
922
 
< 0.1%
Other values (12)56
 
0.1%
ValueCountFrequency (%)
052143
49.3%
131995
30.3%
213471
 
12.7%
35064
 
4.8%
41775
 
1.7%
5699
 
0.7%
6264
 
0.2%
7121
 
0.1%
856
 
0.1%
922
 
< 0.1%
ValueCountFrequency (%)
1971
 
< 0.1%
1341
 
< 0.1%
571
 
< 0.1%
281
 
< 0.1%
232
 
< 0.1%
182
 
< 0.1%
152
 
< 0.1%
143
 
< 0.1%
139
< 0.1%
128
< 0.1%

LACSON, PING (PDR)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.332737115
Minimum0
Maximum225
Zeros9080
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:11.521362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q312
95-th percentile23
Maximum225
Range225
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.616097119
Coefficient of variation (CV)1.034005633
Kurtosis37.6584202
Mean8.332737115
Median Absolute Deviation (MAD)4
Skewness3.598687374
Sum880487
Variance74.23712957
MonotonicityNot monotonic
2022-05-24T17:44:11.642368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09080
 
8.6%
18908
 
8.4%
28782
 
8.3%
38144
 
7.7%
47592
 
7.2%
56744
 
6.4%
66116
 
5.8%
75491
 
5.2%
85001
 
4.7%
94613
 
4.4%
Other values (118)35195
33.3%
ValueCountFrequency (%)
09080
8.6%
18908
8.4%
28782
8.3%
38144
7.7%
47592
7.2%
56744
6.4%
66116
5.8%
75491
5.2%
85001
4.7%
94613
4.4%
ValueCountFrequency (%)
2251
< 0.1%
2181
< 0.1%
2151
< 0.1%
2121
< 0.1%
2021
< 0.1%
1931
< 0.1%
1661
< 0.1%
1641
< 0.1%
1601
< 0.1%
1451
< 0.1%

MANGONDATO, FAISAL (KTPNAN)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct329
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.409034126
Minimum0
Maximum573
Zeros73064
Zeros (%)69.1%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:11.763312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum573
Range573
Interquartile range (IQR)1

Descriptive statistics

Standard deviation17.3092104
Coefficient of variation (CV)7.185124617
Kurtosis218.0171413
Mean2.409034126
Median Absolute Deviation (MAD)0
Skewness13.28802573
Sum254553
Variance299.6087648
MonotonicityNot monotonic
2022-05-24T17:44:11.876335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
073064
69.1%
119366
 
18.3%
25054
 
4.8%
31804
 
1.7%
4947
 
0.9%
5735
 
0.7%
6545
 
0.5%
7409
 
0.4%
8348
 
0.3%
10284
 
0.3%
Other values (319)3110
 
2.9%
ValueCountFrequency (%)
073064
69.1%
119366
 
18.3%
25054
 
4.8%
31804
 
1.7%
4947
 
0.9%
5735
 
0.7%
6545
 
0.5%
7409
 
0.4%
8348
 
0.3%
9281
 
0.3%
ValueCountFrequency (%)
5731
< 0.1%
5631
< 0.1%
5291
< 0.1%
4951
< 0.1%
4611
< 0.1%
4531
< 0.1%
4511
< 0.1%
4431
< 0.1%
4411
< 0.1%
4361
< 0.1%

MARCOS, BONGBONG (PFP)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct851
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.5840289
Minimum0
Maximum1105
Zeros378
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:12.002306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55
Q1191
median304
Q3392
95-th percentile510.75
Maximum1105
Range1105
Interquartile range (IQR)201

Descriptive statistics

Standard deviation138.8758545
Coefficient of variation (CV)0.4730361356
Kurtosis-0.2828640695
Mean293.5840289
Median Absolute Deviation (MAD)98
Skewness0.006904603536
Sum31021850
Variance19286.50297
MonotonicityNot monotonic
2022-05-24T17:44:12.136343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0378
 
0.4%
345355
 
0.3%
364339
 
0.3%
378339
 
0.3%
376337
 
0.3%
343329
 
0.3%
352325
 
0.3%
356324
 
0.3%
340323
 
0.3%
363322
 
0.3%
Other values (841)102295
96.8%
ValueCountFrequency (%)
0378
0.4%
141
 
< 0.1%
224
 
< 0.1%
333
 
< 0.1%
427
 
< 0.1%
542
 
< 0.1%
637
 
< 0.1%
748
 
< 0.1%
842
 
< 0.1%
948
 
< 0.1%
ValueCountFrequency (%)
11051
< 0.1%
10801
< 0.1%
10391
< 0.1%
10261
< 0.1%
10131
< 0.1%
10121
< 0.1%
9641
< 0.1%
9621
< 0.1%
9401
< 0.1%
9341
< 0.1%

MONTEMAYOR, JOSE JR. (DPP)
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5661707645
Minimum0
Maximum37
Zeros66565
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:12.252341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.013811002
Coefficient of variation (CV)1.790645271
Kurtosis92.06014422
Mean0.5661707645
Median Absolute Deviation (MAD)0
Skewness5.415641258
Sum59825
Variance1.027812748
MonotonicityNot monotonic
2022-05-24T17:44:12.346307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
066565
63.0%
126414
 
25.0%
28408
 
8.0%
32635
 
2.5%
4909
 
0.9%
5339
 
0.3%
6148
 
0.1%
789
 
0.1%
842
 
< 0.1%
930
 
< 0.1%
Other values (18)87
 
0.1%
ValueCountFrequency (%)
066565
63.0%
126414
 
25.0%
28408
 
8.0%
32635
 
2.5%
4909
 
0.9%
5339
 
0.3%
6148
 
0.1%
789
 
0.1%
842
 
< 0.1%
930
 
< 0.1%
ValueCountFrequency (%)
371
 
< 0.1%
342
 
< 0.1%
321
 
< 0.1%
301
 
< 0.1%
291
 
< 0.1%
241
 
< 0.1%
232
 
< 0.1%
202
 
< 0.1%
191
 
< 0.1%
185
< 0.1%

PACQUIAO, MANNY PACMAN(PROMDI)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct457
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.31038366
Minimum0
Maximum615
Zeros4461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:12.461090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median14
Q341
95-th percentile140
Maximum615
Range615
Interquartile range (IQR)35

Descriptive statistics

Standard deviation51.21611708
Coefficient of variation (CV)1.492729361
Kurtosis12.30610517
Mean34.31038366
Median Absolute Deviation (MAD)11
Skewness3.050595529
Sum3625441
Variance2623.090648
MonotonicityNot monotonic
2022-05-24T17:44:12.575090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04461
 
4.2%
54170
 
3.9%
34129
 
3.9%
64127
 
3.9%
74063
 
3.8%
44051
 
3.8%
23957
 
3.7%
83867
 
3.7%
13754
 
3.6%
93664
 
3.5%
Other values (447)65423
61.9%
ValueCountFrequency (%)
04461
4.2%
13754
3.6%
23957
3.7%
34129
3.9%
44051
3.8%
54170
3.9%
64127
3.9%
74063
3.8%
83867
3.7%
93664
3.5%
ValueCountFrequency (%)
6151
< 0.1%
6141
< 0.1%
5641
< 0.1%
5541
< 0.1%
5371
< 0.1%
5331
< 0.1%
5301
< 0.1%
5161
< 0.1%
5101
< 0.1%
5081
< 0.1%

ROBREDO, LENI (IND)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct667
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.9256147
Minimum0
Maximum790
Zeros900
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:12.697490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q152
median121
Q3195
95-th percentile357
Maximum790
Range790
Interquartile range (IQR)143

Descriptive statistics

Standard deviation110.3764064
Coefficient of variation (CV)0.7888220229
Kurtosis1.883229974
Mean139.9256147
Median Absolute Deviation (MAD)71
Skewness1.253556496
Sum14785380
Variance12182.95109
MonotonicityNot monotonic
2022-05-24T17:44:12.815531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0900
 
0.9%
25631
 
0.6%
26617
 
0.6%
36587
 
0.6%
38583
 
0.6%
40581
 
0.5%
23578
 
0.5%
24566
 
0.5%
33563
 
0.5%
30561
 
0.5%
Other values (657)99499
94.2%
ValueCountFrequency (%)
0900
0.9%
1304
 
0.3%
2320
 
0.3%
3317
 
0.3%
4362
0.3%
5358
 
0.3%
6406
0.4%
7355
 
0.3%
8401
0.4%
9390
0.4%
ValueCountFrequency (%)
7901
< 0.1%
7441
< 0.1%
7431
< 0.1%
7351
< 0.1%
7321
< 0.1%
7281
< 0.1%
7271
< 0.1%
7182
< 0.1%
6991
< 0.1%
6981
< 0.1%

ratio_robredo_marcos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct55026
Distinct (%)52.2%
Missing303
Missing (%)0.3%
Infinite75
Infinite (%)0.1%
Meaninf
Minimum0
Maximuminf
Zeros597
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size825.6 KiB
2022-05-24T17:44:12.939937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03719200461
Q10.1601365339
median0.4040632054
Q30.8448018528
95-th percentile4.444444444
Maximuminf
Rangeinf
Interquartile range (IQR)0.684665319

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)0.2828054815
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2022-05-24T17:44:13.051940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0597
 
0.6%
1142
 
0.1%
0.5140
 
0.1%
0.3333333333122
 
0.1%
0.291
 
0.1%
0.2586
 
0.1%
0.166666666780
 
0.1%
0.476
 
0.1%
inf75
 
0.1%
0.142857142971
 
0.1%
Other values (55016)103883
98.3%
(Missing)303
 
0.3%
ValueCountFrequency (%)
0597
0.6%
0.0012853470441
 
< 0.1%
0.0013020833331
 
< 0.1%
0.0013280212481
 
< 0.1%
0.0013297872341
 
< 0.1%
0.0013477088951
 
< 0.1%
0.0013568521031
 
< 0.1%
0.0013774104681
 
< 0.1%
0.0013793103451
 
< 0.1%
0.001381215471
 
< 0.1%
ValueCountFrequency (%)
inf75
0.1%
7441
 
< 0.1%
7431
 
< 0.1%
6761
 
< 0.1%
6271
 
< 0.1%
5881
 
< 0.1%
5371
 
< 0.1%
5121
 
< 0.1%
4841
 
< 0.1%
3641
 
< 0.1%

Interactions

2022-05-24T17:44:07.020068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:48.939886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:50.684887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:52.299923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:54.048991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:55.727471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:57.490982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:58.968439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:00.551997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:02.091461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:03.830599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:05.422596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:07.142104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:49.197885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:50.824886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:52.422886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:54.179027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:55.857538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:57.606984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:59.089245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:00.672961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:02.215163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:03.954568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:05.549592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:07.268069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:49.353888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:50.954919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:52.687885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:54.315208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:56.016546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:57.727987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:59.213214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:00.799551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:02.341167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:04.082601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:05.678596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:07.398110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:49.486887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:51.089885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:52.825885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:54.449336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:56.152966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:57.849991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:59.345752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:00.927551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:02.469128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:04.223568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:05.811558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:07.529110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:49.620919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:51.222884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:52.959917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:54.591348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:56.289971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:57.975975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:59.477751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:01.056516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:02.596127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:04.354599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:05.947593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:07.663106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:49.760924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:51.361885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:53.111886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:54.734354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:56.431992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:58.101972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:59.610757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:01.193519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:02.888128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:04.487600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:06.083809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:07.783702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:49.879885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:51.497885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:53.234888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:54.865353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:56.558220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:58.218985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:59.753754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:01.312550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:03.009567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:04.621599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:06.210793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:07.914737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:50.005923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:51.631888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:53.369887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:55.009353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:56.693222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:58.343944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:59.882753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:01.446171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:03.144566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:04.756590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:06.345753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:08.038695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:50.134885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:51.758888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:53.497885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:55.171380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:56.819158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:58.463442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:00.007785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:01.569170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:03.284600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:04.888592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:06.482787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:08.178730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:50.285889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:51.888885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:53.629884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:55.312389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:56.954125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:58.587438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:00.145753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:01.700059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:03.426582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:05.020589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:06.616752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:08.311695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:50.422885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:52.020885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:53.770188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:55.453432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:57.088125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:58.711437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:00.279751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:01.833072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:03.563557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:05.159593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:06.750753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:08.443695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:50.556888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:52.160887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:53.907220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:55.593463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:57.353965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:43:58.843436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:00.416752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:01.963451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:03.697599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:05.292580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-24T17:44:06.887753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-24T17:44:13.162901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-24T17:44:13.347904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-24T17:44:13.550938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-24T17:44:13.724944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-24T17:44:13.820958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-24T17:44:08.675695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-24T17:44:09.115554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-24T17:44:09.464554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

RegionProvinceCityBarangayPrecinct IDABELLA, ERNIE (IND)DE GUZMAN, LEODY (PLM)DOMAGOSO, ISKO MORENO (AKSYON)GONZALES, NORBERTO (PDSP)LACSON, PING (PDR)MANGONDATO, FAISAL (KTPNAN)MARCOS, BONGBONG (PFP)MONTEMAYOR, JOSE JR. (DPP)PACQUIAO, MANNY PACMAN(PROMDI)ROBREDO, LENI (IND)ratio_robredo_marcos
0REGION XBUKIDNONBAUNGONBALINTAD130100092140012240115290.129464
1REGION XBUKIDNONBAUNGONBUENAVISTA130100103575023101188470.151613
2REGION XBUKIDNONBAUNGONDANATAG130100114394302884152440.152778
3REGION XBUKIDNONBAUNGONDANATAG1301001222162301903160510.268421
4REGION XBUKIDNONBAUNGONDANATAG1301001301164602722125460.169118
5REGION XBUKIDNONBAUNGONKALILANGAN1301001460101313200140500.156250
6REGION XBUKIDNONBAUNGONLACOLAC130100153151512751151620.225455
7REGION XBUKIDNONBAUNGONLANGAON130100161040011571146410.261146
8REGION XBUKIDNONBAUNGONLANGAON130100171231311741131360.206897
9REGION XBUKIDNONBAUNGONLIBORAN130100185462402661235510.191729

Last rows

RegionProvinceCityBarangayPrecinct IDABELLA, ERNIE (IND)DE GUZMAN, LEODY (PLM)DOMAGOSO, ISKO MORENO (AKSYON)GONZALES, NORBERTO (PDSP)LACSON, PING (PDR)MANGONDATO, FAISAL (KTPNAN)MARCOS, BONGBONG (PFP)MONTEMAYOR, JOSE JR. (DPP)PACQUIAO, MANNY PACMAN(PROMDI)ROBREDO, LENI (IND)ratio_robredo_marcos
105656REGION IIIBATAANSAMALTABING ILOG8120009004617036701990.269755
105657REGION IIIBATAANSAMALGUGO8120014312502103660111030.281421
105658REGION IIIBATAANSAMALGUGO81200154150080433011870.200924
105659REGION IIIBATAANSAMALGUGO81200165547091413012830.200969
105660REGION IIIBATAANSAMALGUGO812001711441603640141240.340659
105661REGION IIIBATAANSAMALGUGO812001830380703471121240.357349
105662REGION IIIBATAANSAMALWEST CALAGUIMAN (POB.)81200492058251243071970.810700
105663REGION IIIBATAANSAMALWEST CALAGUIMAN (POB.)812005000371100233152361.012876
105664REGION IIIBATAANSAMALWEST DAANG BAGO (POB.)812000331202110194091270.654639
105665REGION IIIBATAANSAMALWEST DAANG BAGO (POB.)812000410262120215021380.641860